Credit Risk Data Scientist

Position Overview

We are seeking a technically strong and analytically minded mid-level

Credit Risk Data Scientist to join our Credit Risk team. The successful candidate will contribute to the development and monitoring of credit risk models, including scorecards, predictive models, and provisioning models, while also supporting portfolio analysis, reporting automation, and dashboard development across the credit lifecycle.

This role offers exposure to a broad range of credit risk analytics responsibilities within an unsecured lending environment. The successful candidate will work with large and complex datasets to generate insights, improve reporting and monitoring processes, and support data-driven credit decisions through analytical investigations and ongoing portfolio monitoring.

Responsibilities

• Assist in the development, enhancement, and monitoring of credit risk models across the credit lifecycle.
• Perform data analysis to identify portfolio trends, anomalies, risk drivers, and opportunities to improve business performance.
• Conduct exploratory data analysis, feature engineering, segmentation analysis, and model performance monitoring.
• Support model validation, back-testing, and ongoing model performance tracking.
• Build, automate, and maintain recurring reports, dashboards, and analytical processes.
• Develop and maintain dashboards and visualisations to provide meaningful insights into portfolio performance, collections trends, and provisioning movements.
• Extract, clean, transform, validate, and reconcile data from multiple data sources.
• Produce regular and ad hoc portfolio monitoring reports and analytical investigations.
• Support the monitoring and optimisation of credit risk strategies using data-driven insights.
• Investigate data irregularities and assist with resolving data quality issues.
• Collaborate with cross-functional teams to translate business requirements into analytical and reporting solutions.
• Contribute to continuous improvement initiatives focused on automation, efficiency, and enhanced risk monitoring.

Qualifications, Experience And Skills

• Degree in Statistics, Mathematics, Data Science, Computer Science, Engineering, Economics, Finance, Actuarial Science, or another quantitative field.
• 3–6 years’ experience in a credit risk, data science, analytics, or quantitative modelling role.
• Demonstrated experience working with credit risk models, scorecards, predictive modelling, or provisioning models within a financial services environment.
• Strong hands-on experience in data analysis, including working with large and complex datasets to generate actionable insights.
• Proven experience in SQL and at least one analytical programming language such as Python or SAS, including data extraction, transformation, and modelling.
• Experience in building, maintaining, and interpreting credit risk models, including performance monitoring, back-testing, and/or validation support.
• Experience in developing dashboards and automated reporting solutions using tools such as Power BI, Tableau, or similar BI tools.
• Strong understanding of credit risk concepts such as scorecards, PD/LGD/EAD models, provisioning, and portfolio monitoring.
• Experience in automating recurring analytical or reporting processes would be highly advantageous.
• Strong ability to interpret results, communicate insights clearly, and translate analytical findings into business recommendations